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GUI主程式.py
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import sys
from PyQt5 import QtWidgets as qw
from PyQt5 import QtGui as qg
from PyQt5 import QtCore as qc
import subprocess
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import math
from math import log
import scipy.stats as ss
import time
import argparse
import glob
import pyBigWig
import threading
class MainWindow(qw.QMainWindow):
def __init__(self):
super(MainWindow,self).__init__()
self.startbtn=qw.QPushButton("Start Analysis")
self.vbbtn=qw.QPushButton("Browser")
self.statuslabel=qw.QLabel("-")
self.progressBar=qw.QProgressBar()
self.statusBar().addPermanentWidget(self.statuslabel)
self.statusBar().addPermanentWidget(self.progressBar)
self.statusBar().addPermanentWidget(self.vbbtn)
self.statusBar().addPermanentWidget(self.startbtn)
self.analysis=RunThread(self.progressBar.value())
self.analysis.count.connect(self.progressBar.setValue)
self.analysis.state.connect(self.statuslabel.setText)
self.startbtn.clicked.connect(self.start)
self.vbbtn.clicked.connect(self.launchb)
def start(self):
self.analysis.start()
def launchb(self):
if(w.c==0):
b.setCentralWidget(DashBoardDefault())
b.show()
else:
b.setCentralWidget(DashBoard())
b.showMaximized()
class Browser(qw.QMainWindow):
def __init__(self):
super(Browser,self).__init__()
class Panel(qw.QWidget):
def __init__(self):
super(Panel,self).__init__()
#para
self.spfpath=''
self.cgmfpath=''
self.igfpath=''
self.wdpath=''
self.c=0
self.rs=''
self.lvv=''
self.cgv=''
self.chhv=''
self.chgv=''
#1
groupBox1 = qw.QGroupBox("Select FIles")
self.label1 = qw.QLabel("-")
btn1=qw.QPushButton("Select Sample List File")
btn1.clicked.connect(self.samplelistfd)
self.label3 = qw.QLabel("-")
btn3=qw.QPushButton("Select Input Gene File")
btn3.clicked.connect(self.inputgenefd)
layout=qw.QGridLayout()
layout.addWidget(self.label1,0,0)
layout.addWidget(btn1,1,0)
layout.addWidget(self.label3,0,1)
layout.addWidget(btn3,1,1)
groupBox1.setLayout(layout)
#2
groupBox2 = qw.QGroupBox("Setteing Parameters")
label4 = qw.QLabel("Min Depth Value")
self.tbn1=qw.QLineEdit("4")
self.tbn1.setValidator(qg.QIntValidator())
label5 = qw.QLabel("Region Size")
label5.setToolTip("smaller region size may let analysis more accurate, but it also takes more time and performence, default is 500")
self.tbn2=qw.QLineEdit("500")
self.tbn2.setValidator(qg.QIntValidator())
label6 = qw.QLabel("Region Quaified Value")
self.tbn3=qw.QLineEdit("4")
self.tbn3.setValidator(qg.QIntValidator())
#dmr_layout
dmr_layout=qw.QHBoxLayout()
label7 = qw.QLabel("DMR_Cut_Off_Value")
label_dmrcg=qw.QLabel("CG")
label_dmrchh=qw.QLabel("CHH")
label_dmrchg=qw.QLabel("CHG")
self.tbn_dmrcg=qw.QLineEdit("0.2")
self.tbn_dmrcg.setValidator(qg.QDoubleValidator())
self.tbn_dmrchh=qw.QLineEdit("0.2")
self.tbn_dmrchh.setValidator(qg.QDoubleValidator())
self.tbn_dmrchg=qw.QLineEdit("0.2")
self.tbn_dmrchg.setValidator(qg.QDoubleValidator())
dmrcg_layout=qw.QHBoxLayout()
dmrcg_layout.addWidget(label_dmrcg)
dmrcg_layout.addWidget(self.tbn_dmrcg)
dmrchh_layout=qw.QHBoxLayout()
dmrchh_layout.addWidget(label_dmrchh)
dmrchh_layout.addWidget(self.tbn_dmrchh)
dmrchg_layout=qw.QHBoxLayout()
dmrchg_layout.addWidget(label_dmrchg)
dmrchg_layout.addWidget(self.tbn_dmrchg)
dmr_layout.addLayout(dmrcg_layout)
dmr_layout.addLayout(dmrchh_layout)
dmr_layout.addLayout(dmrchg_layout)
#heat_layout
heat_layout=qw.QHBoxLayout()
labelpcacut=qw.QLabel("Heatmap_Cut Off Value")
label_heatcg=qw.QLabel("CG")
label_heatchh=qw.QLabel("CHH")
label_heatchg=qw.QLabel("CHG")
self.tbn_heatcg=qw.QLineEdit("0.2")
self.tbn_heatcg.setValidator(qg.QDoubleValidator())
self.tbn_heatchh=qw.QLineEdit("0.2")
self.tbn_heatchh.setValidator(qg.QDoubleValidator())
self.tbn_heatchg=qw.QLineEdit("0.2")
self.tbn_heatchg.setValidator(qg.QDoubleValidator())
heatcg_layout=qw.QHBoxLayout()
heatcg_layout.addWidget(label_heatcg)
heatcg_layout.addWidget(self.tbn_heatcg)
heatchh_layout=qw.QHBoxLayout()
heatchh_layout.addWidget(label_heatchh)
heatchh_layout.addWidget(self.tbn_heatchh)
heatchg_layout=qw.QHBoxLayout()
heatchg_layout.addWidget(label_heatchg)
heatchg_layout.addWidget(self.tbn_heatchg)
heat_layout.addLayout(heatcg_layout)
heat_layout.addLayout(heatchh_layout)
heat_layout.addLayout(heatchg_layout)
labelbinsize=qw.QLabel("Bin Size")
self.tbnbinsize=qw.QLineEdit("1000000")
self.tbnbinsize.setValidator(qg.QIntValidator())
labelpromotor=qw.QLabel("Promotor Size")
self.tbnpromotor=qw.QLineEdit("2000")
self.tbnpromotor.setValidator(qg.QIntValidator())
layout=qw.QGridLayout()
layout.addWidget(label4,0,0)
layout.addWidget(self.tbn1,0,1)
layout.addWidget(label5,1,0)
layout.addWidget(self.tbn2,1,1)
layout.addWidget(label6,2,0)
layout.addWidget(self.tbn3,2,1)
layout.addWidget(label7,3,0)
layout.addLayout(dmr_layout,3,1)
layout.addWidget(labelpcacut,6,0)
layout.addLayout(heat_layout,6,1)
layout.addWidget(labelbinsize,7,0)
layout.addWidget(self.tbnbinsize,7,1)
layout.addWidget(labelpromotor,8,0)
layout.addWidget(self.tbnpromotor,8,1)
groupBox2.setLayout(layout)
#3
groupBox3 = qw.QGroupBox("Choosing Tools")
self.cb1 = qw.QCheckBox("HeatMap & PCA")
self.cb2 = qw.QCheckBox("Enrichment")
self.cb3 = qw.QCheckBox("DMG")
self.cb4 = qw.QCheckBox("ChrView")
self.chrbtn=qw.QPushButton("..")
self.chrbtn.setEnabled(False)
self.chrbtn.clicked.connect(self.launchchrset)
self.cb5 = qw.QCheckBox("MetaPlot")
self.metabtn=qw.QPushButton("..")
self.metabtn.setEnabled(False)
self.metabtn.clicked.connect(self.launchmetaset)
layout=qw.QGridLayout()
layout.addWidget(self.cb1,0,0)
layout.addWidget(self.cb2,1,0)
layout.addWidget(self.cb3,2,0)
layout.addWidget(self.cb4,3,0)
layout.addWidget(self.chrbtn,3,1)
layout.addWidget(self.cb5,4,0)
layout.addWidget(self.metabtn,4,1)
groupBox3.setLayout(layout)
#4
groupBox4 = qw.QGroupBox("Output Setting")
self.label10 = qw.QLabel("./")
self.btn4=qw.QPushButton("Choose Work Directory")
self.btn4.clicked.connect(self.workdirfd)
self.btn4.setEnabled(False)
self.cb6 = qw.QCheckBox("Auto shutdown PC")
layout=qw.QGridLayout()
layout.addWidget(self.label10,0,0)
layout.addWidget(self.btn4,1,0)
layout.addWidget(self.cb6,2,0)
groupBox4.setLayout(layout)
groupBox5 = qw.QGroupBox()
layout2=qw.QGridLayout()
layout2.addWidget(groupBox3,0,0)
layout2.addWidget(groupBox4,0,1)
groupBox5.setLayout(layout2)
#layout
grid1=qw.QGridLayout()
grid1.addWidget(groupBox1,0,0)
grid1.addWidget(groupBox2,1,0)
grid1.addWidget(groupBox5,2,0)
self.setLayout(grid1)
def samplelistfd(self):
self.spfpath, filetype = qw.QFileDialog.getOpenFileName(self,"Select File","./","All Files (*);;Text Files (*.txt)")
self.label1.setText(self.spfpath)
if(self.spfpath!=''):
self.chrbtn.setEnabled(True)
self.metabtn.setEnabled(True)
else:
self.chrbtn.setEnabled(False)
self.metabtn.setEnabled(False)
def inputgenefd(self):
self.igfpath, filetype = qw.QFileDialog.getOpenFileName(self,"Select File","./","All Files (*);;gtf Files (*.gtf)")
self.label3.setText(self.igfpath)
def workdirfd(self):
self.wdpath = qw.QFileDialog.getExistingDirectory(self,"Choose Work Directory")
self.label10.setText(self.wdpath)
def launchchrset(self):
samples = pd.read_csv(self.label1.text(),header=None,sep="\t")
obj=samples.iloc[:,2].unique()
cc.combox1.clear()
cc.combox2.clear()
cc.combox1.addItems(obj)
cc.combox2.addItems(obj)
cc.show()
def launchmetaset(self):
samples = pd.read_csv(self.label1.text(),header=None,sep="\t")
obj=samples.iloc[:,2].unique()
mm.combox1.clear()
mm.combox2.clear()
mm.combox1.addItems(obj)
mm.combox2.addItems(obj)
mm.show()
class DashBoard(qw.QWidget):
def __init__(self):
super(qw.QWidget, self).__init__()
self.layout = qw.QVBoxLayout(self)
# Initialize tab screen
self.tabs = qw.QTabWidget()
#tab1
self.tab1 = qw.QWidget()
console1 = cgtab()
console1.setMinimumSize(1500, 600)
scroll = qw.QScrollArea()
scroll.setWidget(console1)
scroll.setAutoFillBackground(True)
scroll.setWidgetResizable(False)
vbox = qw.QVBoxLayout()
vbox.addWidget(scroll)
self.tab1.setLayout(vbox)
#tab2
self.tab2 = qw.QWidget()
console2 = chhtab()
console2.setMinimumSize(1500, 600)
scroll = qw.QScrollArea()
scroll.setWidget(console2)
scroll.setAutoFillBackground(True)
scroll.setWidgetResizable(False)
vbox = qw.QVBoxLayout()
vbox.addWidget(scroll)
self.tab2.setLayout(vbox)
#tab3
self.tab3 = qw.QWidget()
console3 = chgtab()
console3.setMinimumSize(1500, 600)
scroll = qw.QScrollArea()
scroll.setWidget(console3)
scroll.setAutoFillBackground(True)
scroll.setWidgetResizable(False)
vbox = qw.QVBoxLayout()
vbox.addWidget(scroll)
self.tab3.setLayout(vbox)
self.tabs.addTab(self.tab1,"CG")
self.tabs.addTab(self.tab2,"CHH")
self.tabs.addTab(self.tab3,"CHG")
# Add tabs to widget
self.layout.addWidget(self.tabs)
self.setLayout(self.layout)
class RunThread(qc.QThread):
count=qc.pyqtSignal(int)
state=qc.pyqtSignal(str)
def __init__(self, parent=None):
super(RunThread, self).__init__()
def __del__(self):
self.wait()
def run(self):
mw.startbtn.setEnabled(False)
if(w.spfpath=='' or w.igfpath==''):
w.c=1
print('Required Files are not enough')
mw.startbtn.setEnabled(True)
elif(w.rs=='t' or w.lvv=='t' or w.cgv=='t' or w.chhv=='t' or w.chgv=='t'):
print('Parameters cannot be empty or iligal value')
mw.startbtn.setEnabled(True)
else:
#TotalPipeline
self.state.emit('Preparing..')
path=w.wdpath #working path
heatmap_check=w.cb1.checkState()
enrichment_check=w.cb2.checkState()
dmg_check=w.cb3.checkState()
chrview_check=w.cb4.checkState()
metaplot_check=w.cb5.checkState()
autoshutdown_check=w.cb6.checkState()
self.state.emit('Setting Parameters')
depth=int(w.tbn1.text()) #number of C+T
regionSize=int(w.tbn2.text()) #number of region size
quaifiedSite=int(w.tbn3.text()) #number of least valid region count
pca_heat_cg_cut=float(w.tbn_heatcg.text()) #heatmap least valid max-min to display
pca_heat_chh_cut=float(w.tbn_heatchh.text()) #heatmap least valid max-min to display
pca_heat_chg_cut=float(w.tbn_heatchg.text()) #heatmap least valid max-min to display
cutoff_CG=float(w.tbn_dmrcg.text()) #DMR_CG cutoff value
cutoff_CHH=float(w.tbn_dmrchh.text()) #DMR_CHH cutoff value
cutoff_CHG=float(w.tbn_dmrchg.text()) #DMR_CHG cutoff value
promoter_value=2000 #range of promotor (In front of gene start)
binSize=1000000 #accurucy of chrview and metaplot
input_gene_name=w.label3.text() #input gene file name (obtain path)
self.state.emit('Loading input gene file')
self.count.emit(1)
input_gene=pd.read_csv(w.label3.text(),sep='\t',header=None)
#parser.add_argument('input_genome')
#input_genome=input_genome
#input_gene=input_gene
#samtools faidx ref.fasta %out=fai
#def genome_size(input_genome):
#subprocess.call('''samtools faidx %s'''%(input_genome), shell=True)
#genomefai=pd.read_csv(input_genome+".fai",sep='\t',header=None)
#genome_size=genomefai[1].sum()*1.0
#return genome_size
context=["CG","CHG","CHH"]
#unionsite
combined = pd.DataFrame()
self.state.emit('Reading sample list..')
self.count.emit(2)
samples = pd.read_csv(w.label1.text(),header=None,sep="\t")
self.state.emit('Generating Unionsite Table')
self.count.emit(4)
for sample in samples.itertuples():
#elapsed_time = time.time() - start_time
print("Now processing " + sample[2])
CGmap = pd.read_csv(sample[2], header=None,sep="\t",dtype =
{0:str,2:str,3:str,5:str,6:str,7:int},usecols=[0,2,3,5,6,7],index_col=[0,1,2],compression='gzip')
#elapsed_time = time.time() - start_time
#print("File read " + time.strftime("%H:%M:%S",time.gmtime(elapsed_time)))
CGmap = CGmap.loc[CGmap[7] >= depth,5:5]
#elapsed_time = time.time() - start_time
#print("Filtered " + time.strftime("%H:%M:%S",time.gmtime(elapsed_time)))
sample_col = [sample[1]]
#sample_col = [sample[1] + '_meth',sample[1] + '_mC',sample[1] + '_C']
CGmap.columns = sample_col
if len(combined) == 0:
combined = CGmap
else:
combined = pd.merge(combined,CGmap,left_index=True,right_index=True,how = 'outer')
#elapsed_time = time.time() - start_time
#print("Combined " + time.strftime("%H:%M:%S",time.gmtime(elapsed_time)))
combined.reset_index(inplace=True)
combined.rename(columns = {0:'chr', 2:'pos', 3: 'context'},inplace=True)
combined[['pos']] = combined[['pos']].astype(int)
#process chromosome name
#deleted
combined = combined.sort_values(['chr','pos'],ascending=[True,True])
#elapsed_time = time.time() - start_time
#print("Sorted " + time.strftime("%H:%M:%S",time.gmtime(elapsed_time)))
combined.to_csv('Unionsite.txt',sep = '\t',na_rep='-',index=False)
#common region
self.state.emit('Reading Unionsite File')
self.count.emit(24)
#combined=pd.read_csv('Unionsite.txt',sep='\t')
union=combined
#1.unionsite --> combined
chrs = union['chr'].unique()
self.state.emit('Calculating CommonRegion..')
self.count.emit(26)
#context one by one
for cxt in context:
outfile = 'CommonRegion_' + cxt + '.txt'
with open(outfile,'w') as of:
of.write('\t'.join(['chr','start','end'] + union.columns.values.tolist()[3:]) + "\n")
for chromosome in chrs:
subset = union[(union['context'] == cxt) & (union['chr'] == chromosome)]
maxPos = subset['pos'].max()
bins = range(0,maxPos,regionSize)
groups = subset.groupby(pd.cut(subset['pos'], bins))
for sRange,sValues in groups:
#rows, columns = sValues.shape
#with a region find out minimun sites, less than depthcutoff
minDepth = sValues.iloc[:,3:].count().min()
if minDepth >= quaifiedSite:
methMeth = sValues.iloc[:,3:].astype(float).mean().tolist()
methMeth2 = [("%.3f" %x) for x in methMeth]
start = sRange.left
end = sRange.right
out = map(str,[chromosome,start,end] + methMeth2)
outStr = '\t'.join(out)
of.write(outStr + "\n")
else:
pass
#DMR
filename1=samples.iloc[0,2] #read group name via samples (read in upper step)
filename2_temp=samples
filename2=filename2_temp.iloc[len(filename2_temp.index)-1,2]
self.state.emit('Loading CommonRegion Files')
self.count.emit(46)
file1=pd.read_csv("CommonRegion_CG.txt",sep="\t",header=None)
file2=pd.read_csv("CommonRegion_CHG.txt",sep="\t",header=None)
file3=pd.read_csv("CommonRegion_CHH.txt",sep="\t",header=None)
CG_data=np.array(file1)
CHH_data=np.array(file2)
CHG_data=np.array(file3)
index1_CG=file1.iloc[0,:].str.contains(filename1,regex=False)
index2_CG=file1.iloc[0,:].str.contains(filename2,regex=False)
index1_CG=np.where(np.array(index1_CG)==True)[0]
index2_CG=np.where(np.array(index2_CG)==True)[0]
index1_CHH=file2.iloc[0,:].str.contains(filename1,regex=False)
index2_CHH=file2.iloc[0,:].str.contains(filename2,regex=False)
index1_CHH=np.where(np.array(index1_CHH)==True)[0]
index2_CHH=np.where(np.array(index2_CHH)==True)[0]
index1_CHG=file3.iloc[0,:].str.contains(filename1,regex=False)
index2_CHG=file3.iloc[0,:].str.contains(filename2,regex=False)
index1_CHG=np.where(np.array(index1_CHG)==True)[0]
index2_CHG=np.where(np.array(index2_CHG)==True)[0]
CG_delta=np.array(0.0)
CG_delta=np.append(cutoff_CG,CG_data[1:,index2_CG].astype(np.float).mean(axis=1)-CG_data[1:,index1_CG].astype(np.float).mean(axis=1)) #diffrence of average
CHH_delta=np.array(0.0)
CHH_delta=np.append(cutoff_CHH,CHH_data[1:,index2_CHH].astype(np.float).mean(axis=1)-CHH_data[1:,index1_CHH].astype(np.float).mean(axis=1)) #diffrence of average
CHG_delta=np.array(0.0)
CHG_delta=np.append(cutoff_CHG,CHG_data[1:,index2_CHG].astype(np.float).mean(axis=1)-CHG_data[1:,index1_CHG].astype(np.float).mean(axis=1)) #diffrence of average
CG_data[1:,index1_CG[0]]=CG_data[1:,index1_CG[0]].astype(np.float)+0.0001 #t-test avoid zero condition
CG_data[1:,index2_CG[0]]=CG_data[1:,index2_CG[0]].astype(np.float)+0.0001 #t-test avoid zero condition
CHH_data[1:,index1_CHH[0]]=CHH_data[1:,index1_CHH[0]].astype(np.float)+0.0001 #t-test avoid zero condition
CHH_data[1:,index2_CHH[0]]=CHH_data[1:,index2_CHH[0]].astype(np.float)+0.0001 #t-test avoid zero condition
CHG_data[1:,index1_CHG[0]]=CHG_data[1:,index1_CHG[0]].astype(np.float)+0.0001 #t-test avoid zero condition
CHG_data[1:,index2_CHG[0]]=CHG_data[1:,index2_CHG[0]].astype(np.float)+0.0001 #t-test avoid zero condition
df_a=pd.DataFrame(CG_data[1:,index1_CG]) #preproccess of merge dataframe (convert numpy_array to pandas_dataframe)
df_b=pd.DataFrame(CG_data[1:,index2_CG]) #preproccess of merge dataframe (convert numpy_array to pandas_dataframe)
df_c=pd.DataFrame(CHH_data[1:,index1_CHH]) #preproccess of merge dataframe (convert numpy_array to pandas_dataframe)
df_d=pd.DataFrame(CHH_data[1:,index2_CHH]) #preproccess of merge dataframe (convert numpy_array to pandas_dataframe)
df_e=pd.DataFrame(CHG_data[1:,index1_CHG]) #preproccess of merge dataframe (convert numpy_array to pandas_dataframe)
df_f=pd.DataFrame(CHG_data[1:,index2_CHG]) #preproccess of merge dataframe (convert numpy_array to pandas_dataframe)
idx1 = df_a.index.intersection(df_b.index) #establish index to use in ttest all element together
CG_ttest=np.array(1.1)
CG_ttest=np.append(CG_ttest,ss.stats.ttest_ind(df_a.loc[idx1].astype(np.float), df_b.loc[idx1].astype(np.float), axis=1)[1]) #t-test
idx2 = df_c.index.intersection(df_d.index) #establish index to use in ttest all element together
CHH_ttest=np.array(1.1)
CHH_ttest=np.append(CHH_ttest,ss.stats.ttest_ind(df_c.loc[idx2].astype(np.float), df_d.loc[idx2].astype(np.float), axis=1)[1]) #t-test
idx3 = df_e.index.intersection(df_f.index) #establish index to use in ttest all element together
CHG_ttest=np.array(1.1)
CHG_ttest=np.append(CHG_ttest,ss.stats.ttest_ind(df_e.loc[idx3].astype(np.float), df_f.loc[idx3].astype(np.float), axis=1)[1]) #t-test
CG_data[1:,index1_CG[0]]-=0.0001 #t-test avoid zero condition recovery
CG_data[1:,index2_CG[0]]-=0.0001 #t-test avoid zero condition recovery
CHH_data[1:,index1_CHH[0]]-=0.0001 #t-test avoid zero condition recovery
CHH_data[1:,index2_CHH[0]]-=0.0001 #t-test avoid zero condition recovery
CHG_data[1:,index1_CHG[0]]-=0.0001 #t-test avoid zero condition recovery
CHG_data[1:,index2_CHG[0]]-=0.0001 #t-test avoid zero condition recovery
CGpd=pd.DataFrame(pd.concat([pd.DataFrame(CG_data),pd.DataFrame(CG_delta),pd.DataFrame(CG_ttest)],axis=1,ignore_index=True)) #conbine dataframe
CGpd.columns=pd.concat([file1.iloc[0],pd.DataFrame(['delta','pvalue'])])[0] #fix header name
CGpd=CGpd.drop([0]) #drop 1st row (header name)
CHHpd=pd.DataFrame(pd.concat([pd.DataFrame(CHH_data),pd.DataFrame(CHH_delta),pd.DataFrame(CHH_ttest)],axis=1,ignore_index=True)) #conbine dataframe
CHHpd.columns=pd.concat([file2.iloc[0],pd.DataFrame(['delta','pvalue'])])[0] #fix header name
CHHpd=CHHpd.drop([0]) #drop 1st row (header name)
CHGpd=pd.DataFrame(pd.concat([pd.DataFrame(CHG_data),pd.DataFrame(CHG_delta),pd.DataFrame(CHG_ttest)],axis=1,ignore_index=True)) #conbine dataframe
CHGpd.columns=pd.concat([file3.iloc[0],pd.DataFrame(['delta','pvalue'])])[0] #fix header name
CHGpd=CHGpd.drop([0]) #drop 1st row (header name)
CG_DMRpd=CGpd.loc[np.abs(CGpd['delta'])>=cutoff_CG].loc[CGpd['pvalue']<0.05]
CHH_DMRpd=CHHpd.loc[np.abs(CHHpd['delta'])>=cutoff_CHH].loc[CHHpd['pvalue']<0.05]
CHG_DMRpd=CHGpd.loc[np.abs(CHGpd['delta'])>=cutoff_CHG].loc[CHGpd['pvalue']<0.05]
self.state.emit('Generating DMR Table')
self.count.emit(48)
CG_DMRpd[CG_DMRpd['delta']>0].to_csv('DMR_CG_hyper.txt',sep='\t',index=None)
CG_DMRpd[CG_DMRpd['delta']<0].to_csv('DMR_CG_hypo.txt',sep='\t',index=None)
CG_DMRpd.to_csv('DMR_CG_all.txt',sep='\t',index=None)
CHH_DMRpd[CHH_DMRpd['delta']>0].to_csv('DMR_CHH_hyper.txt',sep='\t',index=None)
CHH_DMRpd[CHH_DMRpd['delta']<0].to_csv('DMR_CHH_hypo.txt',sep='\t',index=None)
CHH_DMRpd.to_csv('DMR_CHH_all.txt',sep='\t',index=None)
CHG_DMRpd[CHG_DMRpd['delta']>0].to_csv('DMR_CHG_hyper.txt',sep='\t',index=None)
CHG_DMRpd[CHG_DMRpd['delta']<0].to_csv('DMR_CHG_hypo.txt',sep='\t',index=None)
CHG_DMRpd.to_csv('DMR_CHG_all.txt',sep='\t',index=None)
if(heatmap_check==2):
#heatmap_PCA
self.state.emit('Graphing heatmap and PCA plot')
self.count.emit(50)
subprocess.call('''Rscript --slave heatmap_PCA_all.R %s %s'''%("CommonRegion_CG.txt",pca_heat_cg_cut), shell=True)
subprocess.call('''Rscript --slave heatmap_PCA_all.R %s %s'''%("CommonRegion_CHG.txt",pca_heat_chh_cut), shell=True)
subprocess.call('''Rscript --slave heatmap_PCA_all.R %s %s'''%("CommonRegion_CHH.txt",pca_heat_chg_cut), shell=True)
else:
self.count.emit(52)
self.count.emit(52)
#transfer txt to bed
def bed_form(inputxt):
subprocess.call('''awk '{if (NR!=1) print $1"\t"$2"\t"$3}' %s > %s'''%(inputxt, inputxt+".bed"), shell=True)
return inputxt+".bed"
#input_gene=input_gene
#input_gene='arabidopsis.gtf'
#annotation name
gene = "Genebody"
exon = "Exon"
intron = "Intron"
utr3 = "3UTR"
utr5 = "5UTR"
cds = "CDS"
promoter = "Promoter"
igr = "IGR"
annotation_name1=[exon,intron,utr5,cds,utr3]
annotation_name=[promoter,gene,exon,intron,utr5,cds,utr3,igr]
#subprocess.call('''gffread %s -T -o %s'''%(input_gene,input_gene+'.gtf'),shell=True)
#enrichment
print ("*--------------------------------*")
print ("|Extract UTR, exon, cds from gene|")
print ("*--------------------------------*")
self.state.emit('Generating enrichment Table')
self.count.emit(52)
subprocess.call('''./extract_transcript_regions.py -i %s -o %s --gtf'''%(input_gene_name,input_gene_name), shell=True)
self.count.emit(54)
print ("*-------------------------------------*")
print ("|Convert this blockbed (bed12) to bed6|")
print ("*-------------------------------------*")
for i in annotation_name1:
subprocess.call('''cat %s | bed12ToBed6 -i stdin -n > %s'''%(input_gene_name+'_'+i+'.bed',input_gene_name+'_'+i+'_bed6.bed'),shell=True)
self.count.emit(56)
#find gene_body.bed
genes=pd.read_csv(input_gene_name, sep='\t',header=None,dtype = {0 :str})
#genes = pd.read_csv(input_gene_name, header=None, sep="\t",dtype = {0 :str})
#genes=genes[genes[0].str.len()<=5]
genes.columns=['chr','unknow', 'exon', 'g_str', 'g_end', 'g_score', 'g_dir','.', 'gene_name']
genes=genes[genes.exon=='exon']
genes.gene_name=' '+genes.gene_name
gene_col=genes['gene_name'].str.split(';', expand=True)
gene_col.columns=gene_col.iloc[0,:]
gene_col[gene_col.index.duplicated()]
gene_col = gene_col[~gene_col.index.duplicated()]
gene_id = gene_col.filter(regex='gene_id')
gene_id = gene_id.ix[:,0].str.split(' ', expand=True)
gene_id[2] = gene_id[2].map(lambda x: x.lstrip('"').rstrip('"'))
gene_id.columns=['num','g_name','gene_id']
gene_bed = genes.ix[:,['chr', 'g_str', 'g_end', 'g_score','g_dir']].join(gene_id['gene_id'])
gene_bed = gene_bed.ix[:,['chr', 'g_str', 'g_end', 'gene_id','g_score','g_dir']]
gene_bed=gene_bed.drop_duplicates(subset=['g_str','g_end'],keep='first')
gene_bed=gene_bed.sort_values(['chr','g_str'],ascending=[True,True])
gene_bed=gene_bed.drop_duplicates(subset=['g_str'],keep='last')
gene_group=gene_bed.groupby(['chr','gene_id','g_score','g_dir']).agg({'g_str':'min', 'g_end':'max'}).reset_index()
gene_group = gene_group.drop_duplicates(subset=['g_str','g_end'],keep='first')
gene_body=gene_group.sort_values(['chr','g_str'],ascending=[True,True])
gene_body=gene_body.drop_duplicates(subset=['g_str'],keep='last')
gene_body = gene_body.ix[:,['chr', 'g_str', 'g_end', 'gene_id','g_score','g_dir']]
#gene_body=gene_body[~gene_body.gene_id.str.contains('MI')]
gene_body.to_csv(input_gene_name+'_Genebody_bed6.bed', sep='\t',index=False, header=None)
self.count.emit(57)
#find promoter.bed
gene_body['pro_str'] = np.where(gene_body.g_dir == '+', gene_body.g_str - promoter_value, gene_body.g_end - 0)
gene_body['pro_end'] = np.where(gene_body.g_dir == '+', gene_body.g_str + 0, gene_body.g_end + promoter_value)
num = gene_body._get_numeric_data()
num[num<0]=0
gene_promoter = gene_body.ix[:, ['chr','pro_str','pro_end','gene_id','g_score','g_dir']]
gene_promoter.columns=['chr','g_str','g_end','gene_id','g_score','g_dir']
gene_promoter.to_csv(input_gene_name+"_Promoter_bed6.bed", sep='\t',index=False, header=None)
self.count.emit(58)
#find igr.bed
gene_body['igr_str'] = gene_body['g_end'].shift(1).fillna(0).astype(int)+1
gene_body['igr_end'] = gene_body['g_str']-1
gene_body['igr_chr'] = gene_body['chr'].shift(1).fillna('chr1')
igrcol = gene_body.ix[:,('igr_chr','g_str','igr_str','igr_end')]
igrcol.columns = ['chr', 'g_str', 'igr_str', 'igr_end']
genecol=gene_body.ix[:,['chr','g_str','g_end','gene_id','g_score','g_dir']]
geneigr = pd.merge(genecol, igrcol, how='left', on=['chr', 'g_str'])
geneigr.igr_str=geneigr.igr_str.fillna(0).astype(int)
geneigr.igr_end=geneigr.igr_end.fillna(geneigr.g_str-1).astype(int)
geneigr = geneigr.ix[:,('chr', 'igr_str','igr_end', 'gene_id','g_score', 'g_dir')]
geneigr = geneigr[geneigr['igr_str']<geneigr['igr_end']]
geneigr=geneigr.drop_duplicates(subset=['igr_str','igr_end'],keep='first')
geneigr.to_csv(input_gene_name+"_IGR_bed6.bed", sep="\t", index=False, header=None)
self.count.emit(59)
for i in annotation_name:
subprocess.call('''bedtools sort -i %s|bedtools merge -c 4,5,6 -o collapse,collapse,collapse >%s '''%(input_gene_name+'_'+i+'_bed6.bed',input_gene_name +'_'+i+'_merge.bed'),shell=True)
self.count.emit(60)
#input_gene_exon_merge.bed
#enrichment
#1. overlap with dmr
def overlap(bed1,bed2):
overlap=subprocess.check_output("bedtools intersect -a %s -b %s |awk '{size+=$3 - $2}END{print size}' "%(bed1,bed2), shell=True)
if len(overlap)<2:
return 1
else:
return int(overlap)
if(enrichment_check==2):
#DMR enrichmet cal & plot
self.state.emit('Graphing enrichment plot')
self.count.emit(60)
for tag in context:
savefile=pd.DataFrame(columns=["dmr","feature","overlap_size","dmr_size","feature_size","genome_size","enrichment"])
for i in annotation_name:
#dmr="DMR_"+tag+"_"+str(dmr_cut)+"_all.txt"
dmr="DMR_"+tag+"_all.txt"
feature=str(i)
overlap_size=overlap(input_gene_name +'_'+i+'_merge.bed',bed_form(dmr))
dmrlen=pd.read_csv(dmr,sep='\t')
if len(dmrlen)<=1:
dmr_size=1000000000000
else:
dmr_size=int(subprocess.check_output('''awk '{size+=$3-$2}END{print size}' %s'''%(bed_form(dmr)), shell=True))
feature_size=overlap(bed_form("CommonRegion_"+tag+".txt"),bed_form(input_gene_name +'_'+i+'_merge.bed'))
genome_size=int(subprocess.check_output('''awk '{size+=$3-$2}END{print size}' %s'''%(bed_form("CommonRegion_"+tag+".txt")), shell=True))*1.0
enrichment=math.log(((float(overlap_size)/float(dmr_size))/(float(feature_size)/float(genome_size))),2)
out = [dmr,feature,overlap_size,dmr_size,feature_size,genome_size,enrichment]
savefile.loc[len(savefile)]=out
savefile.to_csv("enrichment_"+tag+".txt",sep='\t',index=None)
#plot enrichment
#print "\n""Making enrichment plot""\n"
plt.style.use('ggplot')
annotationname = ['Promoter','Genebody','Exon','Intron','5UTR','CDS','3UTR','IGR']
annotationname_index = range(len(annotationname))
Eplt=[savefile['enrichment']]
for value in Eplt:
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ax1.bar(annotationname_index, value,align='center',color='darkblue')
ax1.xaxis.set_ticks_position('bottom')
ax1.yaxis.set_ticks_position('left')
plt.xticks(annotationname_index, annotationname, rotation=90,fontsize='small')
plt.ylabel("Fold Enrichment (log2)")
plt.title(tag+'_Fold_Enrichment')
plt.savefig(tag+'_Fold_Enrichment'+'.png',dpi=400,bbox_inches='tight')
plt.close(fig)
else:
self.count.emit(63)
self.count.emit(63)
#DMG
def dmg(tag):
#subprocess.call('''bedtools intersect -a %s -b %s >%s '''%(input_gene_name +'_gene_body_merge.bed',"DMR_"+tag+"_"+str(dmr_cut)+"_all.txt.bed","DMG_"+tag+"_gene_list.txt"),shell=True)
#subprocess.call('''bedtools intersect -a %s -b %s >%s '''%(input_gene_name +'_gene_promoter_merge.bed',"DMR_"+tag+"_"+str(dmr_cut)+"_all.txt.bed","DMG_"+tag+"_promoter_list.txt"),shell=True)
subprocess.call('''bedtools intersect -a %s -b %s >%s '''%(input_gene_name +'_Genebody_bed6.bed',"DMR_"+tag+"_all.txt.bed","DMG_"+tag+"_gene_list.txt"),shell=True)
subprocess.call('''bedtools intersect -a %s -b %s >%s '''%(input_gene_name +'_Promoter_bed6.bed',"DMR_"+tag+"_all.txt.bed","DMG_"+tag+"_promoter_list.txt"),shell=True)
if(dmg_check==2):
self.state.emit('Generating DMG Table')
self.count.emit(63)
for k in context:
dmg(k)
else:
self.count.emit(64)
self.count.emit(64)
#----------------------------------------------------------------------------------------------------------------------------------------------------------
#chrView
savefile=pd.DataFrame()
if(chrview_check==2):
self.state.emit('Generating chrView Table')
self.count.emit(64)
#combined = pd.DataFrame()
#samples = pd.read_csv("samples_list.txt",header=None,sep="\t")
#minCoverage = 4
#binSize = 1000000
for sample in samples.itertuples():
#print("Now reading " + sample[2])
CGmap = pd.read_csv(sample[2], compression='gzip',header=None,sep="\t",dtype = {0 :str})
chrs = CGmap[0].unique()
Position = 0
#print ("Position\tchromosome\tsRange\tmeanCG\tmeanCHG\tmeanCHH")
with open("chrView_list.txt",'a') as f:
f.write(sample[1]+'\t'+sample[1]+"_chrView.txt"+'\n')
#savefile=pd.DataFrame(columns=["Position","chromosome","sRange","meanCG","meanCHG","meanCHH"])
savefile=pd.DataFrame(columns=["Position","chromosome","start","end","meanCG","meanCHG","meanCHH"])
#mylist=[]
for chromosome in chrs:
subset = CGmap[(CGmap[7] >= depth) & (CGmap[0] == chromosome) ]
maxPos = subset[2].max()
bins = range(0,maxPos,binSize)
groups = subset.groupby(pd.cut(subset[2], bins))
#make list for R draw
#with open("chrView_list.txt",'a') as f:
#f.write(sample[1]+'\t'+sample[1]+"_"+str(binSize)+"_chrView.txt"+'\n':)
for sRange,sValues in groups:
Position += 1
start = sRange.left
end = sRange.right
meanCG = sValues[sValues[3] == 'CG'][5].mean()
meanCHG = sValues[sValues[3] == 'CHG'][5].mean()
meanCHH = sValues[sValues[3] == 'CHH'][5].mean()
out = map(str,[Position,chromosome,start,end,meanCG,meanCHG,meanCHH])
#out = map(str,[Position,chromosome,sRange,meanCG,meanCHG,meanCHH])
#out = pd.Series([Position,chromosome,sRange,meanCG,meanCHG,meanCHH],index=["Position","chromosome","start","end,","meanCG","meanCHG","meanCHH"])
out = pd.Series([Position,chromosome,start,end,meanCG,meanCHG,meanCHH],index=["Position","chromosome","start","end","meanCG","meanCHG","meanCHH"])
savefile=savefile.append(out,ignore_index=True)
#print(sample[1]+"_chrView_2.txt")
savefile.to_csv(sample[1]+"_chrView.txt",sep='\t',index=None)
subprocess.call("Rscript --slave chrView.R" , shell=True)
#chrView_mean
chrlist=pd.read_csv("chrView_list.txt",header=None,sep='\t')
filename1=samples.iloc[0,2] #read group name via samples (read in upper step)
filename2_temp=samples
filename2=filename2_temp.iloc[len(filename2_temp.index)-1,2]
#check split index
si=0
for i in chrlist.iloc[:,1]:
if filename1 in i:
si+=1
else:
break
#load data
data=[]
for i in chrlist.iloc[:,1]:
data.append(pd.read_csv(i,sep='\t'))
mcg1,mchg1,mchh1,mcg2,mchg2,mchh2=0,0,0,0,0,0
for i in range(0,len(chrlist)):
if(i<si):
mcg1+=data[i].iloc[:,4]
mchh1+=data[i].iloc[:,5]
mchg1+=data[i].iloc[:,6]
elif(i>=si):
mcg2+=data[i].iloc[:,4]
mchh2+=data[i].iloc[:,5]
mchg2+=data[i].iloc[:,6]
mcg1/=si
mchg1/=si
mchh1/=si
mcg2/=len(chrlist)-si
mchg2/=len(chrlist)-si
mchh2/=len(chrlist)-si
if(cc.cc1==filename1 and cc.cc2==filename2):
mcg=mcg1-mcg2
mchg=mchg1-mchg2
mchh=mchh1-mchh2
elif(cc.cc1==filename2 and cc.cc2==filename1):
mcg=mcg2-mcg1
mchg=mchg2-mchg1
mchh=mchh2-mchh1
data[0].iloc[:,4]=mcg
data[0].iloc[:,5]=mchg
data[0].iloc[:,6]=mchh
report=data[0]
report.to_csv('chrView_mean.txt',sep='\t',index=False)
pd.DataFrame([[cc.cc1+' - '+cc.cc2,'chrView_mean.txt']]).to_csv('chrView_mean_list.txt',sep='\t',index=False,header=None)
subprocess.call("Rscript --slave chrView-mean.R" , shell=True)
else:
self.count.emit(72)
self.count.emit(72)
#metaplot
if(metaplot_check==2):
#union to bw
#cgmap=pd.read_csv("union4.txt",sep='\t',dtype={0:str})
self.state.emit('Generating metaplot Files')
self.count.emit(72)
cgmap=union
genebodybed=input_gene_name+"_"+mm.mm3+"_merge.bed"
bwheader=[]
for chr in cgmap.iloc[:,0].unique():
k=cgmap[cgmap.iloc[:,0]==chr]
maxPos=k.iloc[:,1].max()
bwheader.append((str(chr),maxPos))
for i in range(3,cgmap.shape[1]):
#print i
Samplename=cgmap.columns[i]
cgoutfile=Samplename+"_CG.bw"
chgoutfile=Samplename+"_CHG.bw"
chhoutfile=Samplename+"_CHH.bw"
bw_CG = pyBigWig.open(cgoutfile,"w")
bw_CG.addHeader(bwheader)
bw_CHG = pyBigWig.open(chgoutfile,"w")
bw_CHG.addHeader(bwheader)
bw_CHH = pyBigWig.open(chhoutfile,"w")
bw_CHH.addHeader(bwheader)
for chromosome in cgmap.iloc[:,0].unique():
k=cgmap[cgmap.iloc[:,0]==chromosome]
CG = k[(k.iloc[:,2] == 'CG') & (k.iloc[:,i] != "-")]
CHG = k[(k.iloc[:,2] == 'CHG') & (k.iloc[:,i] != "-")]
CHH = k[(k.iloc[:,2] == 'CHH') & (k.iloc[:,i] != "-")]
bw_CG.addEntries(str(chromosome),[x-1 for x in CG.ix[:,1].tolist()],values=CG.ix[:,i].astype(float).tolist(), span=1)
bw_CHG.addEntries(str(chromosome),[x-1 for x in CHG.ix[:,1].tolist()],values=CHG.ix[:,i].astype(float).tolist(), span=1)
bw_CHH.addEntries(str(chromosome),[x-1 for x in CHH.ix[:,1].tolist()],values=CHH.ix[:,i].astype(float).tolist(), span=1)
bw_CG.close()
bw_CHG.close()
bw_CHH.close()
subprocess.call('''computeMatrix scale-regions -S %s -R %s -a 2000 -b 2000 -m 4000 -bs 100 -out %s'''%(Samplename+"_CG.bw",genebodybed,Samplename+"_CG.matrix.gz"),shell=True)
subprocess.call('''computeMatrix scale-regions -S %s -R %s -a 2000 -b 2000 -m 4000 -bs 100 -out %s'''%(Samplename+"_CHG.bw",genebodybed,Samplename+"_CHG.matrix.gz"),shell=True)
subprocess.call('''computeMatrix scale-regions -S %s -R %s -a 2000 -b 2000 -m 4000 -bs 100 -out %s'''%(Samplename+"_CHH.bw",genebodybed,Samplename+"_CHH.matrix.gz"),shell=True)
#metaplot
self.state.emit('Graphing metaplot')
self.count.emit(96)
subprocess.call("Rscript --slave metaplot.R "+mm.mm3,shell=True)
subprocess.call("Rscript --slave metaplot-mean.R "+mm.mm3+' '+mm.mm1+' '+mm.mm2,shell=True)
else:
self.count.emit(82)
self.count.emit(82)